├── .gitignore ├── CBOW ├── cbow.py ├── data │ ├── cn.txt │ ├── cn.txt.ebd.npy │ ├── cn.txt.vab │ ├── en.txt │ ├── en.txt.ebd.npy │ └── en.txt.vab ├── data_parser.py └── test_embededword.py ├── LICENSE ├── README.md ├── checkpoints ├── checkpoint ├── dev.data-00000-of-00001 ├── dev.index └── dev.meta ├── data ├── cn.test.txt ├── cn.txt ├── cn.txt.ebd.npy ├── cn.txt.vab ├── code.file ├── en.test.txt ├── en.txt ├── en.txt.ebd.npy ├── en.txt.vab ├── result.txt └── voc.txt ├── doc └── neural machine translation by jointly learning to align and translate.pdf ├── inference.py ├── seq2seq.py ├── utils.py └── 说明文档.docx /.gitignore: -------------------------------------------------------------------------------- 1 | .idea/ 2 | -------------------------------------------------------------------------------- /CBOW/cbow.py: -------------------------------------------------------------------------------- 1 | #coding:utf-8 2 | __author__ = 'jmh081701' 3 | import data_parser 4 | import tensorflow as tf 5 | import numpy as np 6 | import json 7 | 8 | corpus_path = "data\\cn.txt" 9 | embedding_word_path = corpus_path+".ebd" 10 | vacb_path = corpus_path+".vab" 11 | data = data_parser.TextLoader(corpus_path,batch_size=100) 12 | 13 | embedding_word_length = 100 14 | learning_rate =0.0001 15 | #输入 16 | input_x = tf.placeholder(dtype=tf.float32,shape=[None,data.vacb_size],name="inputx") 17 | input_y = tf.placeholder(dtype=tf.float32,shape=[None,data.vacb_size],name='inputy') 18 | W1 = tf.Variable(name="embedding_word",initial_value=tf.truncated_normal(shape=[data.vacb_size,embedding_word_length],stddev=1.0/(data.vacb_size))) 19 | #W1其实就是 词向量矩阵 20 | W2 = tf.Variable(tf.truncated_normal(shape=[embedding_word_length,data.vacb_size],stddev=1.0/data.vacb_size)) 21 | #计算过程 22 | #累加所有的X,然后取平均值，再去乘以词向量矩阵;其效果就是相当于,选择出所有的上下文的词向量,然后取平均 23 | hidden = tf.matmul(input_x,W1) 24 | output = tf.matmul(hidden,W2) #batch_size * vacb_size的大小 25 | output_softmax = tf.nn.softmax(output) 26 | #取出中心词的那个概率值,因为iinput_y是一个one-hot向量,左乘一个列向量,就是将这个列的第i行取出 27 | output_y = tf.matmul(input_y,output_softmax,transpose_b=True) 28 | loss = tf.reduce_sum(- tf.log(output_y)) #将batch里面的output_y累加起来 29 | train_op = tf.train.AdamOptimizer(learning_rate).minimize(loss) 30 | 31 | with tf.Session() as sess: 32 | sess.run(tf.initialize_all_variables()) 33 | max_epoch =100 34 | for epoch in range(1,max_epoch): 35 | _x,_y = data.next_batch() 36 | #生成本次的输入 37 | x =[] 38 | y =[] 39 | for i in range(0,len(_x)): 40 | #将Context*2 -1 个向量,求和取平均为一个向量,用于将来和词向量矩阵相乘 41 | vec = np.zeros(shape=[data.vacb_size]) 42 | for j in range(0,len(_x[i])): 43 | vec[ _x[i][j] ] += 1 44 | vec /= len(_x[i]) 45 | x.append(vec) 46 | y_vec = np.zeros(shape=[data.vacb_size]) 47 | y_vec[_y[i]] = 1 48 | y.append(y_vec) 49 | _loss,_ = sess.run([loss,train_op],feed_dict={input_x:x,input_y:y}) 50 | if (epoch % 100 )==0 : 51 | print({'loss':_loss,'epoch':epoch}) 52 | 53 | #保存词向量 54 | _W1 = sess.run(W1,feed_dict={input_x:[np.zeros([data.vacb_size])],input_y:[np.zeros([data.vacb_size])]}) 55 | #每一行就是对应词的词向量 56 | np.save(embedding_word_path,_W1) 57 | with open(vacb_path,"w",encoding='utf8') as fp: 58 | json.dump(data.inverseV,fp) 59 | 60 | print("Some TEST:") 61 | print(":",_W1[data.V['']]) 62 | print(":",_W1[data.V['']]) 63 | print(":",_W1[data.V['']]) 64 | print("you:",_W1[data.V['you']]) 65 | print("are:",_W1[data.V['are']]) 66 | print("is:",_W1[data.V['is']]) 67 | print("Find Some Word pairs With high similarity") 68 | -------------------------------------------------------------------------------- /CBOW/data/cn.txt.ebd.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmhIcoding/machine_translation/45264d0aa90980ebca5a134a802f025908a6c04d/CBOW/data/cn.txt.ebd.npy -------------------------------------------------------------------------------- /CBOW/data/en.txt.ebd.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmhIcoding/machine_translation/45264d0aa90980ebca5a134a802f025908a6c04d/CBOW/data/en.txt.ebd.npy -------------------------------------------------------------------------------- /CBOW/data_parser.py: -------------------------------------------------------------------------------- 1 | #coding:utf-8 2 | __author__ = 'jmh081701' 3 | import random 4 | class TextLoader(object): 5 | def __init__(self,input_data_path,Context_length=10,batch_size=10,min_frq = 0): 6 | self.Context_length = Context_length #定义上下文的长度 7 | self.V = {} # map word to index 8 | self.inverseV ={} #map index to word 9 | self.raw_text =list() 10 | self.x_data =list() 11 | self.y_data =list() 12 | self.number_batch = 0 13 | self.batch_size = batch_size 14 | 15 | raw_text = [] 16 | #输入原始数据并统计词频 17 | V = dict() #{'word':frq} 18 | with open(input_data_path,"r",encoding="utf8") as fp: 19 | lines = fp.readlines() 20 | for line in lines: 21 | line =line.split(" ") 22 | line = ['']+line+[''] #为每句话加上, 23 | raw_text += line 24 | for word in line: 25 | if word in V: 26 | V[word] +=1 27 | else: 28 | V.setdefault(word,1) 29 | 30 | #清除词频太小的词,同时为各个词建立下标到索引之间的映射 31 | self.V.setdefault('',0) 32 | self.inverseV.setdefault(0,'') 33 | cnt = 1 34 | for word in V: 35 | if V[word] <= min_frq: 36 | continue 37 | else: 38 | self.V.setdefault(word,cnt) 39 | self.inverseV.setdefault(cnt,word) 40 | cnt +=1 41 | self.vacb_size = len(self.V) 42 | #将文本由字符串,转换为词的下标 43 | for word in raw_text: 44 | self.raw_text +=[self.V[word] if word in self.V else self.V['']] 45 | #生成batches 46 | self.gen_batch() 47 | 48 | def gen_batch(self): 49 | self.x_data =[] 50 | self.y_data =[] 51 | for index in range(self.Context_length,len(self.raw_text)-self.Context_length): 52 | #index的前Context,加上index的后Context个词,一起构成了index词的上下文 53 | x = self.raw_text[(index-self.Context_length):index]+ self.raw_text[(index+1):(self.Context_length+index)] 54 | y = [ self.raw_text[index] ] 55 | self.x_data.append(x) 56 | self.y_data.append(y) 57 | self.number_batch =int( len(self.x_data) / self.batch_size) 58 | 59 | def next_batch(self): 60 | batch_pointer = random.randint(0,self.number_batch-1) 61 | x = self.x_data[batch_pointer:(batch_pointer+self.batch_size)] 62 | y = self.y_data[batch_pointer:(batch_pointer+self.batch_size)] 63 | return x ,y 64 | 65 | if __name__ == '__main__': 66 | data = TextLoader("data\\input.en.txt") 67 | print(data.vacb_size) 68 | x,y=data.next_batch() 69 | print(x) 70 | print(y) -------------------------------------------------------------------------------- /CBOW/test_embededword.py: -------------------------------------------------------------------------------- 1 | __author__ = 'jmh081701' 2 | import json 3 | import numpy as np 4 | npy_path = "data//input.en.txt.ebd.npy" 5 | vab_path="data//input.en.txt.vab" 6 | W=np.load(npy_path) 7 | inverseV={}#map word to index 8 | V={} #map index to word 9 | with open(vab_path,encoding='utf8') as fp: 10 | V=json.load(fp) 11 | for each in V: 12 | inverseV.setdefault(V[each],int(each)) 13 | def find_min_word(word,k): 14 | vec = W[inverseV[word]] 15 | tmp=[] 16 | for i in range(len(V)): 17 | tmp.append(np.matmul(vec-W[i].T,(vec-W[i].T).T)) 18 | s =[] 19 | rst=[] 20 | for i in range(len(tmp)): 21 | s.append([tmp[i],i]) 22 | s.sort() 23 | print(s) 24 | for i in range(k): 25 | rst.append(V[str(s[i][1])]) 26 | return rst 27 | print(find_min_word('6',10)) 28 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 任务：用神经网络方法实现中-->英 翻译系统。 2 | 1. 模型要求： 3 | （1）RNN + Attention 模型 4 | （2）Transformer模型 5 | 任选其中一种模型。 6 | 2. 数据及平台： 7 | 8 | (1）数据集： 9 | /data，6834个中英平行语对 10 | /cn.txt和/en.txt的每一行一一对应 11 | (2）模型实现可用平台Tensorflow 或 Pytorch 12 | 13 | 3. 注意： 14 | 本分支是带有注意力机制的，而且对util.py里面的数据预处理部分做了流量领域的相关适配。 15 | 如果需要把模型重新用于机器翻译领域，请修改util.py里面的相关函数。 16 | 17 | 18 | -------------------------------------------------------------------------------- /checkpoints/checkpoint: -------------------------------------------------------------------------------- 1 | model_checkpoint_path: "dev" 2 | all_model_checkpoint_paths: "dev" 3 | -------------------------------------------------------------------------------- /checkpoints/dev.data-00000-of-00001: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmhIcoding/machine_translation/45264d0aa90980ebca5a134a802f025908a6c04d/checkpoints/dev.data-00000-of-00001 -------------------------------------------------------------------------------- /checkpoints/dev.index: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmhIcoding/machine_translation/45264d0aa90980ebca5a134a802f025908a6c04d/checkpoints/dev.index -------------------------------------------------------------------------------- /checkpoints/dev.meta: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmhIcoding/machine_translation/45264d0aa90980ebca5a134a802f025908a6c04d/checkpoints/dev.meta -------------------------------------------------------------------------------- /data/cn.test.txt: -------------------------------------------------------------------------------- 1 | 新华社 北京 3 月 1 日 电 59 岁 的 原 黑龙江省 省长 田凤山 今天 被 任命 为 国土 资源部 部长 。 2 | 出生 于 黑龙江省 肇源县 的 田凤山 1961 年 7 月 参加 工作 , 1970 年 3 月 加入 中国 共产党 。 3 | 这 应该 是 经济 发展 的 自然 扩散 过程 , 和 60 年代 “ 三线 建设 ” 工厂 搬家 是 不同 的 。 4 | 但是 我们 也 要 看到 , 从 转变 资源 配置 方式 的 角度 看 , 改革 的 大关 并 没有 过 。 5 | 最近 , 对于 加入 wto , 一 种 反应 是 盲目乐观 , 认为 无论 怎么 做 中国 都 会 得利 。 6 | 既然 这些 客观 条件 并 不见得 好 的 地方 能够 做到 , 别的 条件 更 好 的 地区 当然 更 可以 做到 。 7 | 全球化 的 客观 趋势 呼唤 全球 范围 的 国际 合作 , 而 全球 合作 已经 远远 超出 了 经济 范围 。 8 | 但 也 要 看到 , 坚定 理想 信念 仍然 是 思想 政治 建设 一 项 十分 现实 而 紧迫 的 任务 。 9 | 尽管 发达 国家 军队 武器 装备 已经 逐步 信息化 , 但 其 军事 理论 、 军队 结构 还 没 发生 质变 。 10 | 我军 应 借鉴 别人 的 经验 , 用 一 种 新 的 机制 促进 各 级 领导 率先 完成 跨越 。 11 | 我军 应 善于 借助 计算机 网络 技术 和 先进 的 模拟 、 仿真 手段 , 在 逼真 的 “ 预实践 ” 中 完成 跨越 12 | 实现 军事 训练 的 跨越 , 最 根本 的 就 是 应 采用 以 信息化 训练 为主 的 现代 训练 方法 与 手段 。 13 | 但 台湾 领导人 在 讲话 中 却 采取 了 回避 和 模糊 的 态度 , 这 是 明显 的 倒退 。 14 | 蒋氏 父子 主政 台湾 几十 年 间 , 始终 声称 “ 中国 只有 一 个 , 中国 必 将 统一 ” 。 15 | 在 谈到 边境 问题 时 , 章启月 说 , 中 印 边境 问题 是 历史 遗留 下来 的 复杂 问题 。 16 | 他 表示 相信 , 米尔扎 的 访问 将 进一步 促进 两 军 尤其是 两 国 海军 之间 的 友好 合作 。 17 | 中国 希望 有关 方面 , 在 重新 考虑 该 条约 时 , 能够 慎重 考虑 条约 对 整个 世界 裁军 进程 的 影响 。 18 | 萨塔尔 是 今天 下午 在 他 下榻 的 钓鱼台 国宾馆 接受 本 社 记者 采访 时 作 上述 表示 的 。 19 | 江 主席 还 参观 了 工厂 、 农庄 和 文化 设施 , 会见 了 旅居 海外 的 华侨 、 华人 代表 。 20 | 当局 的 这 位 人士 振振有辞 地 说 , 岛 内 失业率 不断 攀升 , 与 企业 纷赴 大陆 投资 有 若干 关联 。 21 | 为 配合 这 一 教育 , 由 本 报 主办 的 《 永远 不 变 的 军魂 》 专栏 今天 与 读者 见面 。 22 | 另外 , 美国 想要 回 他们 的 间谍 飞机 也 不 会 是 问题 , 但 可能 需要 一些 时间 。 23 | 其中 , 廿四日 将 上场 的 美台 军售 会议 , 就 是 美国 一 张 立即 可 打 的 牌 。 24 | 他们 把 教育 成果 延伸 到 训练场 , 举办 了 “ 英雄 王伟 告诉 我们 什么 ？ ” 的 演讲会 。 25 | 从 1619 年 第一 批 非洲 黑人 被 运到 新 大陆 开始 , 黑人 便 备受 歧视 , 惨 遭 奴役 。 26 | 如果 这 种 以 他国 为敌 的 冷战 思维 不 改变 , 酞平洋 不 会 酞平 , 整个 世界 也 不 会 安宁 。 27 | 其三 , 发展 和 完善 金融 市场 , 提高 中国 金融 企业 资金 经营 水平 和 社会 资金 使用 效益 。 28 | 参照 国际 上 金融 业务 综合 经营 趋势 , 逐步 完善 中国 金融业 分业 经营 、 分业 监管 的 体制 。 29 | 他 说 , 越共 “ 九 大 ” 的 各 项 准备 工作 已经 就绪 , 相信 大会 一定 会 取得 圆满 成功 。 30 | 要 围绕 增强 城市 整体 功能 和 竞争力 , 把 上海 建设 成为 国际 经济 、 金融 、 贸易 和 航运 中心 之一 。 31 | 不 按 “ 游戏 规则 ” 办事 , 经济 秩序 就 会 大 乱 , 现代化 就 不 可能 实现 。 -------------------------------------------------------------------------------- /data/cn.txt.ebd.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmhIcoding/machine_translation/45264d0aa90980ebca5a134a802f025908a6c04d/data/cn.txt.ebd.npy -------------------------------------------------------------------------------- /data/en.test.txt: -------------------------------------------------------------------------------- 1 | beijing , 1 mar ( xinhua ) -- tian fengshan , former heilongjiang governor who is 59 years old , was appointed minister of land and resources today . 2 | tian fengshan , who was born in zhaoyuan county , heilongjiang province , took part in work since july 1961 and joined the cpc in march 1970 . 3 | this should be a natural process set off by economic development ; the " third tier construction " of the 1960s involving factory relocation was something entirely different . 4 | we must also realize however that from the angle of changing the pattern of resource allocation , we have not yet made the big breakthrough in reform . 5 | with regard to joining the world trade organization , one recent reaction has been blind optimism and the belief that china will profit whatever it does . 6 | since these areas where objective conditions are not particularly good can achieve this , other areas where conditions are better can naturally do the same . 7 | the objective trend of globalization is calling for international cooperation on a global scale , and a global cooperation has far exceeded the scope of the economy . 8 | it should be noted , however , that strengthening our ideals and convictions remains a very practical and urgent task in ideological and political construction . 9 | even if the military weaponry and equipment of developed countries have already gradually moved toward informationization , military theory and military structure have not shown any qualitative change . 10 | china 's military should refer to the experience of others and use new mechanisms to promote the priority completion of the informationization leap by leaders at all levels . 11 | china 's military should become adept at using computer networks and advanced simulation and scenario methods to complete the informationization leap at the " pre-reality line . " 12 | to realize a leap in military training , the most fundamental issue is that contemporary training methods should mainly employ measures that use informationization training . 13 | yet , the taiwan leader took an evasive and vague position [ on the one-china principle ] in his speech ; this was an obvious retrogression . 14 | when chiang kai-shek and his son ruled taiwan for several decades , they always claimed that " there is only one china and china must unified . " 15 | on the border issue , zhang qiyue said : the issue on borders between china and india is a complicated one left over from history . 16 | he said he believed mirza 's visit would continue to enhance the friendship and cooperation between the armed forces , especially the navies , of the two countries . 17 | china hopes that when reconsidering the treaty , relevant sides will be prudent and consider the impact of the treaty on the disarmament process of the whole world . 18 | foreign minister abdus sattar made the above remarks when xinhua reporters interviewed him this afternoon at the diaoyutai state guest house where he is staying . 19 | president jiang has also paid visits to factories , farms , or cultural facilities and has met with representatives of overseas chinese and foreign nationals of chinese descent . 20 | this personage of the taiwan authorities said plausibly that the unceasing rising unemployment rate on the island is related to enterprises ' successive investments in mainland china . 21 | starting today , our daily will dedicate a section , entitled , " the military spirit that never changes , " to assisting the advancement of this campaign . 22 | in addition , there will not be a problem for the united states to take back their spy plane . but it might take time . 23 | among them , the us-taiwan meeting on arms sales , to be held on 24 april , is a card which the united states can play immediately . 24 | they extended the results of the education to the training field and held a speech session entitled " what did hero wang wei tell us ? " 25 | since the first blacks from africa were shipped to the new world in 1619 , they were subject to discrimination and tragically forced into slavery . 26 | unless this cold war mentality of regarding other countries as enemies changes , there will be no peace in the pacific and no tranquillity in the world . 27 | third , we will develop and improve financial markets , and enhance the level of capital management and social capital efficiency of chinese financial enterprises . 28 | following the trend of comprehensive management of international financial business , we must gradually improve the operation and management systems for separate sectors by the chinese financial industry . 29 | he said : various preparations for the upcoming congress have been under way and he is convinced that the congress will be a successful one . 30 | it should focus its efforts on expanding overall urban functions and competitiveness and build itself into one of the international economic , financial , trade and shipping centers . 31 | if things are not handled according to the " rules of the game , " the economic order will be chaotic and modernization cannot be realized . -------------------------------------------------------------------------------- /data/en.txt.ebd.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmhIcoding/machine_translation/45264d0aa90980ebca5a134a802f025908a6c04d/data/en.txt.ebd.npy -------------------------------------------------------------------------------- /data/voc.txt: -------------------------------------------------------------------------------- 1 | the 13682 2 | . 7316 3 | , 7289 4 | and 6851 5 | of 6262 6 | to 4296 7 | in 3770 8 | " 2791 9 | a 2524 10 | is 1958 11 | that 1772 12 | 's 1422 13 | on 1394 14 | china 1376 15 | for 1350 16 | has 1010 17 | with 971 18 | this 941 19 | have 905 20 | will 897 21 | by 845 22 | as 827 23 | be 824 24 | are 762 25 | it 749 26 | people 706 27 | he 675 28 | at 674 29 | taiwan 672 30 | development 665 31 | we 633 32 | not 618 33 | an 617 34 | : 587 35 | said 585 36 | chinese 557 37 | countries 542 38 | two 539 39 | also 525 40 | us 518 41 | from 516 42 | new 515 43 | should 503 44 | all 500 45 | was 495 46 | its 490 47 | their 488 48 | economic 473 49 | relations 445 50 | united 425 51 | states 410 52 | party 401 53 | our 392 54 | between 365 55 | out 362 56 | one 347 57 | government 346 58 | his 345 59 | they 337 60 | must 329 61 | can 320 62 | military 311 63 | been 309 64 | work 309 65 | more 308 66 | cooperation 304 67 | important 295 68 | national 291 69 | up 291 70 | which 289 71 | world 280 72 | only 268 73 | or 265 74 | other 264 75 | some 262 76 | but 261 77 | year 259 78 | ' 258 79 | state 256 80 | country 255 81 | years 250 82 | international 247 83 | there 245 84 | jiang 245 85 | made 237 86 | system 234 87 | foreign 230 88 | three 225 89 | time 219 90 | political 218 91 | were 216 92 | president 212 93 | meeting 204 94 | great 203 95 | after 202 96 | visit 201 97 | first 200 98 | over 195 99 | issue 192 100 | security 190 101 | policy 190 102 | li 188 103 | had 187 104 | about 185 105 | no 184 106 | zemin 184 107 | make 184 108 | situation 181 109 | however 180 110 | social 179 111 | trade 179 112 | these 178 113 | economy 173 114 | since 173 115 | century 171 116 | i 171 117 | committee 168 118 | both 167 119 | if 166 120 | law 165 121 | into 164 122 | sides 164 123 | many 164 124 | rights 164 125 | enterprises 162 126 | under 161 127 | various 161 128 | central 161 129 | japan 160 130 | developing 159 131 | would 157 132 | when 155 133 | during 154 134 | who 154 135 | further 154 136 | still 153 137 | efforts 153 138 | war 152 139 | defense 149 140 | principle 149 141 | than 148 142 | support 148 143 | such 148 144 | forces 147 145 | market 144 146 | reform 144 147 | human 143 148 | held 143 149 | through 142 150 | beijing 142 151 | pointed 141 152 | peace 140 153 | region 139 154 | so 135 155 | leaders 133 156 | ) 131 157 | -@@ 131 158 | good 131 159 | become 130 160 | zhu 130 161 | very 130 162 | according 128 163 | major 128 164 | long 127 165 | them 126 166 | reunification 126 167 | education 126 168 | well 125 169 | plan 124 170 | even 124 171 | interests 123 172 | ( 123 173 | now 123 174 | issues 123 175 | members 121 176 | against 121 177 | last 120 178 | hong 119 179 | russia 119 180 | ? 118 181 | today 118 182 | building 117 183 | use 116 184 | kong 116 185 | bush 115 186 | being 114 187 | most 113 188 | ; 112 189 | side 112 190 | cadres 112 191 | force 111 192 | independence 110 193 | do 110 194 | what 109 195 | always 108 196 | stability 108 197 | financial 107 198 | may 107 199 | future 107 200 | order 106 201 | cross-strait 106 202 | current 106 203 | authorities 106 204 | past 105 205 | history 105 206 | while 105 207 | -- 105 208 | areas 105 209 | friendly 105 210 | western 104 211 | expressed 104 212 | continue 104 213 | ] 104 214 | [ 104 215 | mainland 103 216 | falungong 103 217 | public 103 218 | any 102 219 | strategic 102 220 | percent 101 221 | importance 100 222 | information 100 223 | because 99 224 | level 98 225 | cpc 98 226 | same 98 227 | common 98 228 | role 98 229 | activities 98 230 | affairs 97 231 | problems 97 232 | talks 97 233 | conditions 97 234 | develop 97 235 | present 96 236 | ed 96 237 | way 95 238 | own 95 239 | technology 95 240 | bilateral 93 241 | friendship 92 242 | strengthen 92 243 | joint 92 244 | ing 90 245 | in@@ 90 246 | how 90 247 | exchanges 89 248 | necessary 89 249 | chairman 89 250 | air 87 251 | represents 87 252 | promote 86 253 | wto 86 254 | departments 86 255 | measures 86 256 | making 85 257 | fundamental 85 258 | means 85 259 | socialist 85 260 | speech 84 261 | basic 84 262 | period 84 263 | systems 84 264 | progress 83 265 | motherland 83 266 | management 83 267 | rongji 83 268 | practice 83 269 | growth 83 270 | minister 82 271 | power 82 272 | toward 82 273 | take 81 274 | part 81 275 | promoting 81 276 | quality 80 277 | just 80 278 | wang 80 279 | forward 80 280 | scientific 79 281 | fully 79 282 | peaceful 78 283 | large 78 284 | understanding 77 285 | ties 77 286 | nation 77 287 | recent 77 288 | dprk 77 289 | army 76 290 | able 76 291 | relevant 76 292 | missile 76 293 | strong 76 294 | end 76 295 | modernization 75 296 | cannot 75 297 | general 75 298 | chen 75 299 | could 75 300 | investment 75 301 | those 74 302 | stressed 74 303 | times 74 304 | society 74 305 | re@@ 74 306 | environment 74 307 | levels 74 308 | ated 74 309 | put 74 310 | d@@ 73 311 | peng 73 312 | k@@ 73 313 | each 73 314 | control 73 315 | number 73 316 | basis 73 317 | you 73 318 | question 73 319 | high 73 320 | attention 73 321 | council 72 322 | tibet 72 323 | improve 71 324 | developed 71 325 | supervision 71 326 | opening 71 327 | among 71 328 | down 71 329 | policies 71 330 | yuan 71 331 | mutual 71 332 | construction 70 333 | views 70 334 | es 70 335 | regional 69 336 | sino-us 69 337 | dis@@ 69 338 | billion 69 339 | article 69 340 | secretary 69 341 | whether 69 342 | set 69 343 | historical 68 344 | b@@ 68 345 | second 68 346 | increase 68 347 | personnel 68 348 | p@@ 68 349 | come 67 350 | administration 67 351 | training 67 352 | certain 67 353 | leadership 67 354 | another 67 355 | de@@ 67 356 | including 66 357 | him 66 358 | process 66 359 | japanese 66 360 | office 66 361 | following 66 362 | science 66 363 | day 65 364 | one-china 65 365 | un@@ 65 366 | weapons 65 367 | key 64 368 | change 64 369 | leading 64 370 | special 64 371 | npc 64 372 | legal 63 373 | ideological 63 374 | un 63 375 | exchange 63 376 | resources 63 377 | study 63 378 | different 62 379 | again 62 380 | premier 62 381 | therefore 62 382 | cause 62 383 | results 62 384 | arms 62 385 | before 62 386 | report 61 387 | industry 61 388 | m@@ 61 389 | concerned 61 390 | ate 61 391 | c@@ 60 392 | serious 60 393 | although 60 394 | water 60 395 | without 60 396 | t 60 397 | five-year 60 398 | fields 60 399 | ted 59 400 | five 59 401 | does 59 402 | carry 59 403 | positive 59 404 | theory 59 405 | hope 59 406 | see 59 407 | conference 59 408 | s 58 409 | cultural 58 410 | news 58 411 | organizations 58 412 | struggle 58 413 | overall 58 414 | nations 58 415 | achievements 58 416 | along 57 417 | position 57 418 | course 57 419 | strategy 57 420 | changes 57 421 | h@@ 56 422 | action 56 423 | spirit 56 424 | leader 56 425 | hold 56 426 | technological 56 427 | diplomatic 56 428 | ating 56 429 | lee 56 430 | e@@ 55 431 | sovereignty 55 432 | 1 55 433 | india 55 434 | strait 55 435 | did 55 436 | matter 55 437 | south 55 438 | play 55 439 | already 55 440 | city 55 441 | established 55 442 | asia 54 443 | problem 54 444 | er 54 445 | ago 54 446 | point 54 447 | units 54 448 | rural 54 449 | s@@ 53 450 | thinking 53 451 | advanced 53 452 | space 53 453 | main 53 454 | media 53 455 | effective 53 456 | strengthening 53 457 | line 53 458 | fact 52 459 | whole 52 460 | maintain 52 461 | standing 52 462 | never 52 463 | direct 52 464 | clinton 52 465 | help 52 466 | hu 52 467 | member 52 468 | incident 52 469 | ation 52 470 | vice 52 471 | official 51 472 | o@@ 51 473 | y 51 474 | officials 51 475 | right 51 476 | masses 51 477 | ers 51 478 | full 51 479 | shanghai 51 480 | research 51 481 | task 51 482 | life 51 483 | 20 51 484 | hard 51 485 | domestic 50 486 | believe 50 487 | i@@ 50 488 | reached 50 489 | things 50 490 | organization 50 491 | 10 50 492 | need 50 493 | open 50 494 | armed 50 495 | like 49 496 | stand 49 497 | difficult 49 498 | foundation 49 499 | operations 49 500 | nmd 49 501 | summit 49 502 | zhang 48 503 | her 48 504 | adopted 48 505 | understand 48 506 | third 48 507 | election 48 508 | group 48 509 | delegation 48 510 | democratic 48 511 | agreement 48 512 | visited 47 513 | reporter 47 514 | carried 47 515 | shui-bian 47 516 | ting 47 517 | structure 47 518 | big 47 519 | every 47 520 | shall 47 521 | implement 47 522 | circles 47 523 | exercise 46 524 | land 46 525 | strength 46 526 | cooperative 46 527 | total 46 528 | russian 46 529 | agricultural 46 530 | impact 46 531 | reason 46 532 | your 46 533 | 1999 46 534 | stage 45 535 | result 45 536 | en@@ 45 537 | l@@ 45 538 | far 45 539 | session 45 540 | local 45 541 | million 45 542 | implementation 45 543 | f@@ 45 544 | given 45 545 | west 45 546 | give 45 547 | act 45 548 | g@@ 45 549 | regulations 45 550 | wei 44 551 | decision 44 552 | e 44 553 | trend 44 554 | establishment 44 555 | dialogue 44 556 | laws 44 557 | working 44 558 | treaty 44 559 | requirements 44 560 | asian 44 561 | early 44 562 | department 44 563 | equipment 44 564 | build 44 565 | themselves 44 566 | peoples 44 567 | taking 44 568 | possible 44 569 | asked 43 570 | higher 43 571 | responsibility 43 572 | characteristics 43 573 | better 43 574 | plane 43 575 | ensure 43 576 | congress 43 577 | once 43 578 | achieved 43 579 | facts 43 580 | ts 43 581 | qian 43 582 | significance 43 583 | project 43 584 | here 43 585 | nature 43 586 | tasks 43 587 | thus 42 588 | socialism 42 589 | production 42 590 | willing 42 591 | capital 42 592 | several 42 593 | teng-hui 42 594 | con@@ 42 595 | achieve 42 596 | n@@ 42 597 | responsible 42 598 | organs 42 599 | ministry 42 600 | globalization 42 601 | modern 41 602 | deputy 41 603 | correct 41 604 | especially 41 605 | few 41 606 | east 41 607 | provide 41 608 | korean 41 609 | relationship 41 610 | parties 41 611 | opportunities 41 612 | taken 41 613 | forum 41 614 | nuclear 41 615 | concern 40 616 | regard 40 617 | recently 40 618 | bring 40 619 | july 40 620 | business 40 621 | 4 40 622 | within 40 623 | global 40 624 | face 40 625 | series 40 626 | as@@ 40 627 | 5 40 628 | reports 40 629 | then 40 630 | small 40 631 | competition 40 632 | go 40 633 | met 40 634 | trust 39 635 | where 39 636 | 10th 39 637 | place 39 638 | pay 39 639 | indicated 39 640 | contacts 39 641 | li@@ 39 642 | al@@ 39 643 | rok 39 644 | culture 39 645 | meet 39 646 | $ 39 647 | realize 39 648 | soon 39 649 | half 39 650 | based 39 651 | influence 39 652 | success 39 653 | greater 39 654 | innovation 39 655 | 2 39 656 | o 38 657 | paid 38 658 | demand 38 659 | administrative 38 660 | back 38 661 | state-owned 38 662 | establish 38 663 | carrying 38 664 | knowledge 38 665 | view 38 666 | used 38 667 | governments 38 668 | status 38 669 | she 38 670 | products 38 671 | addition 38 672 | stable 38 673 | el@@ 37 674 | prime 37 675 | facing 37 676 | 3 37 677 | attitude 37 678 | consensus 37 679 | much 37 680 | issued 37 681 | days 37 682 | enterprise 37 683 | groups 37 684 | 50 37 685 | province 37 686 | 2000 37 687 | middle 36 688 | an@@ 36 689 | clearly 36 690 | workers 36 691 | methods 36 692 | create 36 693 | ad@@ 36 694 | constantly 36 695 | democracy 36 696 | enhance 36 697 | comrade 36 698 | hopes 36 699 | related 36 700 | comprehensive 36 701 | regions 36 702 | protection 36 703 | prosperity 36 704 | ch@@ 36 705 | territory 36 706 | took 35 707 | handling 35 708 | company 35 709 | xinhua 35 710 | experience 35 711 | opportunity 35 712 | points 35 713 | ninth 35 714 | cppcc 35 715 | putin 35 716 | entire 35 717 | t@@ 35 718 | cold 35 719 | links 35 720 | noted 35 721 | a@@ 35 722 | industrial 35 723 | june 35 724 | say 35 725 | funds 35 726 | young 35 727 | di@@ 35 728 | long-term 35 729 | unity 35 730 | respect 35 731 | clear 35 732 | p 35 733 | close 35 734 | next 34 735 | improvement 34 736 | commission 34 737 | rapid 34 738 | my 34 739 | contributions 34 740 | extremely 34 741 | qichen 34 742 | upon 34 743 | four 34 744 | combat 34 745 | together 34 746 | ethnic 34 747 | service 34 748 | core 34 749 | move 34 750 | wu 34 751 | center 34 752 | it@@ 34 753 | step 34 754 | mainly 34 755 | fourth 33 756 | so-called 33 757 | learned 33 758 | capability 33 759 | sales 33 760 | continued 33 761 | old 33 762 | successful 33 763 | provided 33 764 | case 33 765 | propaganda 33 766 | compatriots 33 767 | american 33 768 | needs 33 769 | am 33 770 | experts 33 771 | complete 33 772 | maintaining 33 773 | committees 33 774 | goal 33 775 | principles 33 776 | tang 33 777 | off 33 778 | played 33 779 | troops 33 780 | itself 33 781 | above 32 782 | accordance 32 783 | march 32 784 | relatively 32 785 | doing 32 786 | conduct 32 787 | community 32 788 | european 32 789 | left 32 790 | form 32 791 | cases 32 792 | maintained 32 793 | sea 32 794 | 8 32 795 | projects 32 796 | opinion 32 797 | cult 32 798 | factors 32 799 | sense 32 800 | aircraft 32 801 | jintao 32 802 | large-scale 32 803 | marxism 32 804 | missiles 32 805 | officers 32 806 | or@@ 32 807 | words 31 808 | actively 31 809 | religious 31 810 | show 31 811 | jointly 31 812 | provincial 31 813 | plans 31 814 | area 31 815 | grain 31 816 | deal 31 817 | am@@ 31 818 | d 31 819 | reporters 31 820 | 7 31 821 | l 31 822 | overseas 31 823 | improving 31 824 | negotiations 31 825 | turn 31 826 | 21st 31 827 | why 31 828 | generation 31 829 | ding 31 830 | j@@ 31 831 | hand 31 832 | u@@ 31 833 | resolutely 31 834 | going 31 835 | communist 31 836 | be@@ 31 837 | implementing 31 838 | 1@@ 30 839 | en 30 840 | 6 30 841 | led 30 842 | kind 30 843 | actual 30 844 | criminal 30 845 | im@@ 30 846 | expand 30 847 | firmly 30 848 | thought 30 849 | ma@@ 30 850 | amount 30 851 | 4@@ 30 852 | peasants 30 853 | increased 30 854 | ability 30 855 | cities 30 856 | north 30 857 | mechanism 30 858 | 5@@ 30 859 | representatives 30 860 | majority 30 861 | y@@ 30 862 | ations 29 863 | started 29 864 | age 29 865 | korea 29 866 | increasing 29 867 | macao 29 868 | beginning 29 869 | internet 29 870 | brought 29 871 | emphasized 29 872 | due 29 873 | commercial 29 874 | questions 29 875 | rather 29 876 | profound 29 877 | at@@ 29 878 | using 29 879 | network 29 880 | currently 29 881 | ic@@ 29 882 | 100 29 883 | objective 29 884 | al 29 885 | stance 29 886 | inspection 29 887 | real 29 888 | operation 29 889 | iraq 29 890 | localities 28 891 | ahead 28 892 | road 28 893 | expanding 28 894 | hoped 28 895 | ab@@ 28 896 | difficulties 28 897 | outside 28 898 | challenges 28 899 | april 28 900 | effectively 28 901 | october 28 902 | greatly 28 903 | internal 28 904 | called 28 905 | raise 28 906 | peninsula 28 907 | shows 28 908 | tax 28 909 | per@@ 28 910 | traditional 28 911 | wish 27 912 | deng 27 913 | saying 27 914 | integrity 27 915 | start 27 916 | practical 27 917 | highly 27 918 | entry 27 919 | productive 27 920 | field 27 921 | command 27 922 | remarks 27 923 | ds 27 924 | join 27 925 | know 27 926 | interest 27 927 | meanwhile 27 928 | others 27 929 | effort 27 930 | rate 27 931 | deputies 27 932 | stated 27 933 | decided 27 934 | reality 27 935 | ever 27 936 | speed 27 937 | hongzhi 27 938 | seek 27 939 | want 27 940 | ant 27 941 | gradually 27 942 | program 27 943 | export 27 944 | get 26 945 | repeatedly 26 946 | immediately 26 947 | w@@ 26 948 | als 26 949 | r@@ 26 950 | island 26 951 | caused 26 952 | regarding 26 953 | wars 26 954 | person 26 955 | yet 26 956 | companies 26 957 | living 26 958 | healthy 26 959 | having 26 960 | ir@@ 26 961 | created 26 962 | technical 26 963 | continuously 26 964 | benefit 26 965 | staff 26 966 | normal 26 967 | exist 26 968 | opinions 26 969 | threat 26 970 | m 26 971 | safeguard 26 972 | began 26 973 | xinjiang 26 974 | visiting 26 975 | services 26 976 | safeguarding 26 977 | particularly 26 978 | yang 26 979 | laid 26 980 | on@@ 26 981 | 9 26 982 | attack 26 983 | le@@ 26 984 | holding 26 985 | theoretical 26 986 | completely 25 987 | expected 25 988 | prospects 25 989 | morning 25 990 | ap@@ 25 991 | moreover 25 992 | increasingly 25 993 | certainly 25 994 | constitution 25 995 | asia-pacific 25 996 | changed 25 997 | reforms 25 998 | kinds 25 999 | crisis 25 1000 | er@@ 25 1001 | sectors 25 1002 | seriously 25 1003 | preparations 25 1004 | purpose 25 1005 | enemy 25 1006 | tion 25 1007 | ar@@ 25 1008 | fine 25 1009 | energy 25 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| ong 18 1413 | labor 18 1414 | recognize 18 1415 | finally 18 1416 | directly 18 1417 | cuba 18 1418 | washington 18 1419 | truly 18 1420 | safety 18 1421 | limited 18 1422 | procedures 18 1423 | risks 18 1424 | british 18 1425 | era 18 1426 | tr@@ 18 1427 | potential 17 1428 | su@@ 17 1429 | fall 17 1430 | persons 17 1431 | fight 17 1432 | almost 17 1433 | deepening 17 1434 | abroad 17 1435 | arrangements 17 1436 | basically 17 1437 | principal 17 1438 | school 17 1439 | union 17 1440 | 22 17 1441 | built 17 1442 | nor 17 1443 | rise 17 1444 | partnership 17 1445 | th@@ 17 1446 | enhancing 17 1447 | six 17 1448 | speaking 17 1449 | 23 17 1450 | lack 17 1451 | economies 17 1452 | vitality 17 1453 | self-@@ 17 1454 | unification 17 1455 | holds 17 1456 | poor 17 1457 | wife 17 1458 | letter 17 1459 | personal 17 1460 | express 17 1461 | enter 17 1462 | ter 17 1463 | huge 17 1464 | pre@@ 17 1465 | truth 17 1466 | eastern 17 1467 | method 17 1468 | no@@ 17 1469 | palestine 17 1470 | 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-------------------------------------------------------------------------------- 1 | __author__ = 'jmh081701' 2 | import tensorflow as tf 3 | import numpy as np 4 | from src.encoder_decoder.utils import DATAPROCESS 5 | batch_size = 100 6 | dataGen = DATAPROCESS(batch_size=batch_size, 7 | is_test= True, 8 | seperate_rate= 0.0 9 | ) 10 | #所有的test里面的样本都拿去测试,seperate_rate 于是应该是100%,表示所有的样本都分离开来了 11 | 12 | loaded_graph = tf.Graph() 13 | with tf.Session(graph=loaded_graph) as sess: 14 | # Load saved model 15 | loader = tf.train.import_meta_graph('checkpoints/dev.meta') 16 | loader.restore(sess, tf.train.latest_checkpoint('./checkpoints')) 17 | 18 | input_data = loaded_graph.get_tensor_by_name('inputs:0') 19 | logits = loaded_graph.get_tensor_by_name('predictions:0') 20 | target_sequence_length = loaded_graph.get_tensor_by_name('target_sequence_len:0') 21 | source_sequence_length = loaded_graph.get_tensor_by_name('source_sequence_len:0') 22 | print("inference begin ") 23 | print("inference") 24 | 25 | dataGen.epoch = 1 26 | epoch = dataGen.epoch 27 | while epoch == dataGen.epoch: 28 | output_x,output_label,src_sequence_length,dst_sequence_length=dataGen.next_train_batch() 29 | 30 | 31 | translate_logits=sess.run(fetches=logits,feed_dict={input_data:output_x,target_sequence_length:dst_sequence_length,source_sequence_length:src_sequence_length}) 32 | 33 | for i in range(len(translate_logits)): 34 | obv=[dataGen.tokenid_to_size(output_x[i][j]) for j in range(len(output_x[i]))] 35 | 36 | label = [dataGen.tokenid_to_size(output_label[i][j]) for j in range(len(output_label[i]))] 37 | pre=[dataGen.tokenid_to_size(translate_logits[i][j]) * int(label[j]!=0) for j in range(len(translate_logits[i]))] 38 | print('src length:{0},dst length:{1}'.format(src_sequence_length[i],dst_sequence_length[i])) 39 | print({'obv':obv}) 40 | print({'label':label}) 41 | print({'prediction':pre}) 42 | input('For next line, press enter.') 43 | -------------------------------------------------------------------------------- /seq2seq.py: -------------------------------------------------------------------------------- 1 | #coding:utf8 2 | __author__ = 'jmh081701' 3 | from src.encoder_decoder.utils import DATAPROCESS 4 | import tensorflow as tf 5 | import datetime 6 | from tensorflow.python.ops import array_ops 7 | batch_size = 100 8 | rnn_size = 200 9 | rnn_num_layers = 1 10 | 11 | max_epoch = 500 12 | 13 | 14 | encoder_embedding_size = 50 15 | decoder_embedding_size = 50 16 | # Learning Rate 17 | lr = 1e-3 18 | factor = 0.98 #学习速率的衰减因子 19 | display_step = 10 20 | dataGen = DATAPROCESS(batch_size=batch_size, 21 | seperate_rate=0 22 | ) 23 | 24 | def model_inputs(): 25 | inputs = tf.placeholder(tf.int32, [batch_size, dataGen.src_sentence_length], name="inputs") 26 | targets = tf.placeholder(tf.int32, [batch_size, dataGen.dst_sentence_length], name="targets") 27 | learning_rate = tf.placeholder(tf.float32, name="learning_rate") 28 | 29 | source_sequence_len = tf.placeholder(tf.int32, (batch_size,), name="source_sequence_len") 30 | target_sequence_len = tf.placeholder(tf.int32, (batch_size,), name="target_sequence_len") 31 | max_target_sequence_len = tf.placeholder(tf.int32, (batch_size,), name="max_target_sequence_len") 32 | 33 | return inputs, targets, learning_rate, source_sequence_len, target_sequence_len, max_target_sequence_len 34 | 35 | def encoder_layer(rnn_inputs, rnn_size, rnn_num_layers, 36 | source_sequence_len, source_vocab_size, encoder_embedding_size=100): 37 | 38 | # 对输入的单词进行词向量嵌入 39 | encoder_embed = tf.contrib.layers.embed_sequence(rnn_inputs, source_vocab_size, encoder_embedding_size) 40 | #print(encoder_embed.shape) 41 | # LSTM单元 42 | def get_lstm(rnn_size): 43 | lstm = tf.contrib.rnn.LSTMCell(rnn_size, initializer=tf.random_uniform_initializer(-0.1, 0.1, seed=123)) 44 | return lstm 45 | 46 | # 堆叠rnn_num_layers层LSTM 47 | lstms = tf.contrib.rnn.MultiRNNCell([get_lstm(rnn_size) for _ in range(rnn_num_layers)]) 48 | encoder_outputs, encoder_states = tf.nn.dynamic_rnn(lstms, encoder_embed, 49 | sequence_length=source_sequence_len, 50 | dtype=tf.float32) 51 | #print(encoder_outputs.shape,encoder_states.shape) 52 | return encoder_outputs, encoder_states 53 | 54 | def decoder_layer_inputs(target_data, batch_size): 55 | """ 56 | 对Decoder端的输入进行处理 57 | 58 | @param target_data: 目标语数据的tensor 59 | @param target_vocab_to_int: 目标语数据的词典到索引的映射: dict 60 | @param batch_size: batch size 61 | """ 62 | # 去掉batch中每个序列句子的最后一个单词 63 | ending = tf.strided_slice(target_data, [0, 0], [batch_size, -1], [1, 1]) 64 | 65 | decoder_inputs = tf.concat([tf.fill([batch_size, 1], dataGen.start_token_id),ending], 1) 66 | 67 | return decoder_inputs 68 | 69 | 70 | def decoder_layer(encoder_states, decoder_inputs, target_sequence_len, 71 | max_target_sequence_len, rnn_size, rnn_num_layers, 72 | target_vocab_size, decoder_embedding_size, batch_size, 73 | start_id,end_id,encoder_outputs): 74 | 75 | decoder_embeddings = tf.Variable(tf.random_uniform([target_vocab_size, decoder_embedding_size])) 76 | decoder_embed_input = tf.nn.embedding_lookup(decoder_embeddings, decoder_inputs) 77 | 78 | def get_lstm(rnn_size): 79 | lstm = tf.contrib.rnn.LSTMCell(rnn_size, initializer=tf.random_uniform_initializer(-0.1, 0.1, seed=456)) 80 | return lstm 81 | 82 | decoder_cell = tf.contrib.rnn.MultiRNNCell([get_lstm(rnn_size) for _ in range(rnn_num_layers)]) 83 | 84 | 85 | output_layer = tf.layers.Dense(target_vocab_size) 86 | 87 | with tf.variable_scope("decoder"): 88 | training_helper = tf.contrib.seq2seq.TrainingHelper(inputs=decoder_embed_input, 89 | sequence_length=target_sequence_len, 90 | time_major=False) 91 | print('88,',decoder_embed_input.shape) 92 | #添加注意力机制 93 | #先定义一个Bahda 注意力机制。它是用一个小的神经网络来做打分的,num_units指明这个小的神经网络的大小 94 | attention_mechanism = tf.contrib.seq2seq.BahdanauAttention(num_units=rnn_size,memory=encoder_outputs,memory_sequence_length=source_sequence_len) 95 | #在原来rnn的基础上配上一层AttentionWrapper 96 | decoder_cell_train = tf.contrib.seq2seq.AttentionWrapper(decoder_cell,attention_mechanism,attention_layer_size=rnn_size) 97 | #初始状态设置为encoder最后的输出状态 98 | #初始状态设置为encoder最后的输出状态 99 | training_decoder = tf.contrib.seq2seq.BasicDecoder(decoder_cell_train, 100 | training_helper, 101 | decoder_cell_train.zero_state(batch_size,dtype=tf.float32).clone(cell_state=encoder_states), 102 | output_layer) 103 | print('good94,max_target_sequence_len',max_target_sequence_len) 104 | training_decoder_outputs, _,_ = tf.contrib.seq2seq.dynamic_decode(training_decoder, 105 | impute_finished=False, 106 | maximum_iterations=max_target_sequence_len) 107 | print('98:',training_decoder_outputs.rnn_output.shape) 108 | print('good98') 109 | with tf.variable_scope("decoder", reuse=True): 110 | 111 | start_tokens = tf.tile(tf.constant([dataGen.start_token_id], dtype=tf.int32), [batch_size], name="start_tokens") 112 | inference_helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(decoder_embeddings, 113 | start_tokens, 114 | dataGen.end_token_id) 115 | print('good105') 116 | attention_mechanism_infer = tf.contrib.seq2seq.BahdanauAttention(num_units=rnn_size,memory=encoder_outputs,memory_sequence_length=source_sequence_len) 117 | #加入attention ，加入的方式与train类似 118 | decoder_cell_infer = tf.contrib.seq2seq.AttentionWrapper(decoder_cell,attention_mechanism_infer,attention_layer_size=rnn_size) 119 | inference_decoder = tf.contrib.seq2seq.BasicDecoder(decoder_cell_infer, 120 | inference_helper, 121 | decoder_cell_infer.zero_state(batch_size,dtype=tf.float32).clone(cell_state=encoder_states), 122 | output_layer) 123 | print('good110') 124 | inference_decoder_outputs, _,_ = tf.contrib.seq2seq.dynamic_decode(inference_decoder, 125 | impute_finished=False, 126 | maximum_iterations=max_target_sequence_len) 127 | print('good114') 128 | 129 | return training_decoder_outputs, inference_decoder_outputs 130 | 131 | 132 | 133 | def seq2seq_model(input_data, target_data, batch_size, 134 | source_sequence_len, target_sequence_len, max_target_sentence_len, 135 | source_vocab_size, target_vocab_size, 136 | encoder_embedding_size, decoder_embeding_size, 137 | rnn_size, rnn_num_layers): 138 | encoder_outputs, encoder_states = encoder_layer(input_data, rnn_size, rnn_num_layers, source_sequence_len, 139 | source_vocab_size, encoder_embedding_size) 140 | 141 | decoder_inputs = decoder_layer_inputs(target_data, batch_size) 142 | 143 | training_decoder_outputs, inference_decoder_outputs = decoder_layer(encoder_states=encoder_states, 144 | decoder_inputs=decoder_inputs, 145 | target_sequence_len=target_sequence_len, 146 | max_target_sequence_len=max_target_sentence_len, 147 | rnn_size=rnn_size, 148 | rnn_num_layers=rnn_num_layers, 149 | target_vocab_size=target_vocab_size, 150 | decoder_embedding_size=decoder_embeding_size, 151 | batch_size=batch_size, 152 | start_id=dataGen.start_token_id, 153 | end_id=dataGen.end_token_id, 154 | encoder_outputs=encoder_outputs) 155 | print('good139') 156 | return training_decoder_outputs, inference_decoder_outputs 157 | 158 | 159 | train_graph = tf.Graph() 160 | 161 | with train_graph.as_default(): 162 | inputs, targets, learning_rate, source_sequence_len, target_sequence_len, _ = model_inputs() 163 | 164 | max_target_sequence_len = dataGen.dst_sentence_length 165 | train_logits, inference_logits = seq2seq_model( 166 | input_data= inputs, 167 | target_data= targets, 168 | batch_size=batch_size, 169 | source_sequence_len=source_sequence_len, 170 | target_sequence_len=target_sequence_len, 171 | max_target_sentence_len=max_target_sequence_len, 172 | source_vocab_size=dataGen.vocb_size, 173 | target_vocab_size= dataGen.vocb_size, 174 | encoder_embedding_size= encoder_embedding_size, 175 | decoder_embeding_size= decoder_embedding_size, 176 | rnn_size=rnn_size, 177 | rnn_num_layers=rnn_num_layers) 178 | 179 | training_logits = tf.identity(train_logits.rnn_output, name="logits") 180 | inference_logits = tf.identity(inference_logits.sample_id, name="predictions") 181 | 182 | print('170:',target_sequence_len.get_shape()) 183 | with tf.name_scope("optimization"): 184 | current_ts = tf.to_int32(tf.minimum(tf.shape(targets)[1], tf.shape(training_logits)[1])) 185 | # 对 target 进行截取 186 | target_sequences = tf.slice(targets, begin=[0, 0], size=[-1, current_ts]) 187 | masks = tf.sequence_mask(lengths=target_sequence_len, maxlen=current_ts, dtype=tf.float32) 188 | training_logits = tf.slice(training_logits, begin=[0, 0, 0], size=[-1, current_ts, -1]) 189 | 190 | inference_sample_id = tf.slice(inference_logits, begin=[0, 0], size=[-1, current_ts]) 191 | #valid_loss = tf.reduce_mean(tf.equal(target_sequences,inference_sample_id)) 192 | cost = tf.contrib.seq2seq.sequence_loss(training_logits, 193 | target_sequences, 194 | masks) 195 | 196 | optimizer = tf.train.AdamOptimizer(learning_rate) 197 | 198 | gradients = optimizer.compute_gradients(cost) 199 | clipped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None] 200 | train_op = optimizer.apply_gradients(clipped_gradients) 201 | 202 | with tf.Session(graph=train_graph) as sess: 203 | sess.run(tf.global_variables_initializer()) 204 | try: 205 | loader = tf.train.Saver() 206 | loader.restore(sess, tf.train.latest_checkpoint('./checkpoints')) 207 | print('Load from a history model') 208 | except Exception as exp: 209 | print("Train a new model") 210 | saver = tf.train.Saver() 211 | dataGen.epoch =1 212 | step = 1 213 | epoch = dataGen.epoch 214 | while dataGen.epoch < max_epoch: 215 | output_x,output_label,src_sequence_length,dst_sequence_length=dataGen.next_train_batch() 216 | _, loss = sess.run( 217 | [train_op, cost], 218 | {inputs: output_x, 219 | targets: output_label, 220 | learning_rate: lr, 221 | source_sequence_len: src_sequence_length, 222 | target_sequence_len: dst_sequence_length}) 223 | if dataGen.train_batch_index % display_step == 0 and dataGen.train_batch_index > 0: 224 | print('{0} : Epoch {1:>3} Step {3} - Loss On 验证集: {2:>6.4f}' 225 | .format(datetime.datetime.now(),dataGen.epoch, loss,step)) 226 | 227 | step += 1 228 | if epoch != dataGen.epoch : 229 | epoch = dataGen.epoch 230 | lr *=factor 231 | saver.save(sess,"checkpoints/dev") 232 | #每次epoch保存一次模型 233 | # Save Model 234 | saver.save(sess, "checkpoints/dev") 235 | print('Model Trained and Saved') 236 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | __author__ = 'jmh081701' 2 | import json 3 | import copy 4 | import numpy as np 5 | import random 6 | 7 | from src.df.src.utility import LoadDataWalkieTalkieCW 8 | from src.df.src.utility import Cluster_consecutive_bursts 9 | 10 | class DATAPROCESS: 11 | def __init__(self, 12 | seperate_rate=0.05, 13 | batch_size=100, 14 | observe_length=10, 15 | vocb_size=9520, 16 | is_test=False 17 | ): 18 | 19 | self.obv_length = observe_length 20 | self.seperate_rate =seperate_rate #验证集占比 21 | self.batch_size = batch_size 22 | #data structure to build 23 | self.src_data_raw=[] #全部数据集 24 | self.dst_data_raw =[] 25 | self.src_train_raw=[] #训练集 26 | self.dst_train_raw = [] 27 | self.src_test_raw =[] #测试集 28 | self.dst_test_raw =[] 29 | 30 | self.src_word_embeddings=None #中文词 词向量以及词典 31 | self.src_id2word=None 32 | self.src_word2id=None 33 | self.src_embedding_length =0 34 | 35 | self.dst_word_embeddings=None #英文 词向量以及词典 36 | self.dst_id2word=None 37 | self.dst_word2id=None 38 | self.dst_embedding_length =0 39 | 40 | self.src_cdf=[] #源语 句子长度的累计分布 src_cdf[t]=0.6,表示 P({x<=t}) = 60% 41 | self.dst_cdf=[] #目标语 句子长度的累计分布 dst_cdf[t]=0.6,表示 P({x<=t}) = 60% 42 | 43 | self.src_sentence_length = 40 #encode 输入序列的长度 44 | self.dst_sentence_length = 40 #decode 输出结果序列的长度 45 | 46 | self.last_batch=0 47 | self.epoch =0 48 | self.vocb_size = vocb_size #每个burst_size 属于有正也有负 49 | 50 | self.start_size_id = vocb_size -1 #起始标识 51 | self.end_size_id = 0 #结束标注 52 | self.end_token_id = self.size_to_tokenid(self.end_size_id) 53 | self.start_token_id = self.size_to_tokenid(self.start_size_id) 54 | 55 | self.__load_wordebedding() 56 | self.__load_data(is_test=is_test) 57 | 58 | def size_to_tokenid(self,burst_size): 59 | #把负方向的busrt size 映射到4758维以上 60 | if burst_size < 0: 61 | return self.vocb_size//2 - burst_size 62 | else: 63 | return burst_size 64 | def tokenid_to_size(self,tokenid): 65 | if tokenid >self.vocb_size//2: 66 | return self.vocb_size//2 - tokenid 67 | else: 68 | return tokenid 69 | def __load_wordebedding(self): 70 | print('There is no need to load pretrained embedding wordvector') 71 | pass 72 | def cdf(self,length_list,percentile): 73 | return np.percentile(length_list,percentile) 74 | def _real_length(self,x): 75 | ##x.shape:(1,2300,1) 76 | x = np.array(x) 77 | l= 0 78 | r = x.shape[0] 79 | while l < r: 80 | mid = (l+r)//2 81 | if abs(x[mid])> 0: 82 | l =mid+1 83 | else: 84 | r = mid 85 | return l 86 | def __load_data(self,is_test = False): 87 | 88 | X_train, y_train, X_valid, y_valid, X_test, y_test=LoadDataWalkieTalkieCW(is_cluster=False) 89 | if not is_test: 90 | X=np.concatenate([X_train,X_valid]) 91 | else: 92 | X=X_test 93 | 94 | raw_text = Cluster_consecutive_bursts(X,normalized=False,padding=True,return_array=False) 95 | #每一行就是一个句子,而且是填充好的. 96 | 97 | train_data_rawlines = copy.deepcopy(raw_text) 98 | train_label_rawlines = copy.deepcopy(raw_text) 99 | del raw_text 100 | 101 | total_lines = len(train_data_rawlines) 102 | assert len(train_data_rawlines)==len(train_label_rawlines) 103 | src_len=[] 104 | dst_len=[] 105 | for index in range(total_lines): 106 | #add and seperate valid ,train set. 107 | data= [self.start_size_id] + train_label_rawlines[index] 108 | data =data[:self.src_sentence_length] #把起始的start_burst_size_id加上,然后截断为固定的长度 109 | 110 | if self._real_length(data) <= self.obv_length: 111 | #过滤掉小于观察长度的数据 112 | continue 113 | data=[self.size_to_tokenid(x) for x in data] #把burst_size_id转换为token的表示方法, 114 | #print('full data:',data) 115 | label = copy.deepcopy(data[self.obv_length:]) #这是输入到decoder的 116 | #print('label:',label) 117 | src_len.append(self._real_length(data)) 118 | dst_len.append(self._real_length(label)) 119 | # 120 | data=data[:self.obv_length] #+[self.end_token_id]*(self.src_sentence_length-self.obv_length) #截断!只保留[0,obv_length),obv_length之后的内容全部为0，这是输入为encoder的 121 | #print('obv:',data) 122 | self.src_data_raw.append(data) 123 | self.dst_data_raw.append(label) 124 | 125 | if random.uniform(0,1)